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English(EN) Qwen3.8-27B: faster than vLLM and SGLang on one GPU

Backboard.io引擎在Qwen3.8-27B推理上优于vLLM、SGLang

作者 PulseAugur 编辑部 · [1 个来源] ·

Backboard.io开发了一个新的推理引擎，在单GPU上运行Qwen3.8-27B模型时，其吞吐量和延迟优于vLLM和SGLang。该引擎在各种输入和输出token配置下均取得了优异的结果，在首个token的中位数时间和端到端延迟方面有显著提升。Backboard.io将其引擎的性能归功于专有的4位草稿头（用于token提议）和全BF16模型验证，以及INT8绑定的LM头和自定义解码GEMM内核。 AI

影响 这一发展可能带来更高效、更快速的大型语言模型推理，从而降低运营成本并改善用户体验。

排序理由 该条目详细介绍了推理引擎的性能基准和技术实现，属于AI基础设施的研究与开发范畴。[lever_c_demoted from research: ic=1 ai=1.0]

在 dev.to — LLM tag 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

Backboard.io引擎在Qwen3.8-27B推理上优于vLLM、SGLang

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该条目详细介绍了推理引擎的性能基准和技术实现，属于AI基础设施的研究与开发范畴。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. dev.to — LLM tag TIER_1 English(EN) · Jonathan Murray ·

    Qwen3.8-27B: 单GPU上比vLLM和SGLang更快

    <p>Big models are where inference engines show off. There's a lot of room to win.</p> <p>27B is harder. The model is small, the GPU isn't the bottleneck in the same way, and every engine starts to look alike.</p> <p>So that's where we went next. And we brought SGLang along.</p> <…

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